Decentralized Congestion Control (DCC) mechanisms have been a core part of protocol stacks for vehicular networks since their inception and standardization. The ETSI ITS-G5 protocol stack for vehicular communications considers the usage of DCC not only in the network or access layers, but also as a part of the cross-layer architecture that influences how often messages are generated and transmitted. ETSI DCC mechanisms have evolved from a reactive approach based on a finite state machine, to an adaptive approach that relies on a linear control algorithm. This linear control algorithm, called LIMERIC, is the basis of the mechanism used in the ETSI DCC Adaptive Approach. The behavior of this algorithm depends on a set of parameters. Different values for these parameters have been proposed in the literature, including those defined in the ETSI specification. A recent proposal is Dual-$\alpha$, which chooses parameters to improve convergence and fairness when the algorithm has to react to fast changes in the use of the shared medium (transitory situations). This article evaluates, by means of simulations, the performance of the ETSI DCC Adaptive Approach and related algorithms, considering both steady state and transitory situations. Results show that a bad selection of parameters can make a DCC algorithm ineffective, that the ETSI DCC Adaptive algorithm performs well in steady state conditions, and that Dual-$\alpha$ performs as well in steady state conditions and outperforms the ETSI DCC Adaptive Approach in transitory scenarios.

翻译：去中心化拥塞控制（DCC）机制自其提出及标准化以来，一直是车载网络协议栈的核心组成部分。用于车载通信的ETSI ITS-G5协议栈不仅考虑在网络层或接入层使用DCC，还将其作为跨层架构的一部分，影响消息生成和传输的频率。ETSI DCC机制已从基于有限状态机的反应式方法，发展为依赖线性控制算法的自适应方法。该线性控制算法称为LIMERIC，是ETSI DCC自适应方法所采用机制的基础。该算法的行为取决于一组参数。文献中已提出这些参数的不同取值，包括ETSI规范中定义的参数。最近提出了Dual-$\alpha$方法，该方法通过选择参数来改善算法在应对共享媒介使用快速变化（瞬态情况）时的收敛性和公平性。本文通过仿真评估了ETSI DCC自适应方法及相关算法在稳态和瞬态情况下的性能。结果表明，参数选择不当可能导致DCC算法失效；ETSI DCC自适应算法在稳态条件下表现良好；而Dual-$\alpha$在稳态条件下表现同样出色，并在瞬态场景中优于ETSI DCC自适应方法。